Uncertainty analysis for multi-stereo 3d shape estimation

Cecco, M. De; Baglivo, L.; Parzianello, G.; Lunardelli, Massimo; Setti, Francesco; Pertile, Marco

doi:10.1109/amuem.2009.5207608

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…We presented a general method for trinocular 3D reconstruction and outlier rejection. A further improvement could be the investigation of uncertainty of the SIFT features detector that may improve the outlier filtering by means of compatibility analysis (De Cecco et al 2009). An experimental setup has been built and tests have been performed in order to estimate system performances.…”

Section: Discussionmentioning

confidence: 99%

Shape measurement system for single point incremental forming (SPIF) manufacts by using trinocular vision and random pattern

Setti¹,

Bini²,

Lunardelli³

et al. 2012

Meas. Sci. Technol.

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Many contemporary works show the interest of scientific community in measuring shape of artefacts made by Single Point Incremental Forming (SPIF). In this paper we will present an algorithm able to detect feature points with random pattern, check the compatibility of associations exploiting multistereo constraints and reject outliers, and perform a 3d reconstruction by dense random patterns. The algorithm is suitable for a real-time application, in fact it needs just three images and a synchronous relatively fast processing. The proposed method has been tested on a simple geometry and results have been compared with a Coordinates Measurement Machine (CMM) acquisition.

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Section: Discussionmentioning

confidence: 99%

Shape measurement system for single point incremental forming (SPIF) manufacts by using trinocular vision and random pattern

Setti¹,

Bini²,

Lunardelli³

et al. 2012

Meas. Sci. Technol.

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“…Wu et al and Fooladgar et al demonstrated that quantization error is highly dependent on these factors by modelling the uncertainty volumes (see preceding section) rather than just the total magnitude in (1). Uncertainty volumes have also been used in other studies as a way to communicate the probabilistic nature of calculated 3-D point positions, for example De Cecco et al generated uncertainty ellipsoids to take into account the uncertainties from calibration and stereo matching [28].…”

Section: Geometric Constraints On Localization Errormentioning

confidence: 99%

Volumetric Calculation of Quantization Error in 3-D Vision Systems

Bohacek,

Coates,

Selviah

2020

Preprint

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This paper investigates how the inherent quantization of camera sensors introduces uncertainty in the calculated position of an observed feature during 3-D mapping. It is typically assumed that pixels and scene features are points, however, a pixel is a two-dimensional area that maps onto multiple points in the scene. This uncertainty region is a bound for quantization error in the calculated point positions. Earlier studies calculated the volume of two intersecting pixel views, approximated as a cuboid, by projecting pyramids and cones from the pixels into the scene. In this paper, we reverse this approach by generating an array of scene points and calculating which scene points are detected by which pixel in each camera. This enables us to map the uncertainty regions for every pixel correspondence for a given camera system in one calculation, without approximating the complex shapes. The dependence of the volumes of the uncertainty regions on camera baseline length, focal length, pixel size, and distance to object, shows that earlier studies overestimated the quantization error by at least a factor of two. For static camera systems the method can also be used to determine volumetric scene geometry without the need to calculate disparity maps.

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“…This was chosen instead of a multi-stereo approach that can provide a better accuracy, if uncertainty is properly estimated as in [6,7]. Differently, multi-camera provides smoother points trajectories while multi-stereo reconstruction tends to generate marker "jumps" from one stereo pair to another, which ends up with also a jump in the corresponding 3D position.…”

Section: Image Processing For Shape Reconstructionmentioning

confidence: 99%

Garment-based motion capture (GaMoCap): high-density capture of human shape in motion

Biasi

Setti

Bue

et al. 2015

Machine Vision and Applications

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This paper presents a new motion capture (MoCap) system, the garment-based motion capture system -GaMoCap. The key feature is the use of an easily wearable garment printed with colour-coded pattern and a generic multicamera setup with standard video cameras. The coded pattern allows a high-density distribution of markers per unit of surface (about 40 markers per 100 cm 2 ), avoiding markers-swap errors. The high density of markers reconstructed makes possible a simultaneous reconstruction of shape and motion, which gives several concurrent advantages with respect to the state of the art and providing performances comparable with previous marker-based systems. In particular, we provide effective solutions to counter the soft-tissue artefact which is a common problem for garment-based techniques. This effect is reduced using Point Cluster Technique to filter out the points strongly affected by non-rigid motion. Uncertainty of motion estimation has been experimentally quantified by comparing with a state-of-the-art commercial system and numerically predicted by means of a Monte Carlo Method procedure. The experimental evaluation was performed on three different articulated motions: shoulder, knee and hip flexion-extension. The results shows that for the three The work was supported by the motion angles estimated with GaMoCap, the system provides comparable accuracies against a commercial VICON system.

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Uncertainty analysis for multi-stereo 3d shape estimation

Cited by 5 publications

References 7 publications

Shape measurement system for single point incremental forming (SPIF) manufacts by using trinocular vision and random pattern

Shape measurement system for single point incremental forming (SPIF) manufacts by using trinocular vision and random pattern

Volumetric Calculation of Quantization Error in 3-D Vision Systems

Garment-based motion capture (GaMoCap): high-density capture of human shape in motion

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